Besplatna dostava putem Box Now paketomata i Overseas kurirske službe iznad 69,99 €.

Provjerite status narudžbe

Pridružite se zajednici ljubitelja knjige iz cijelog svijeta i ostvarite mnoštvo pogodnosti. Izradite besplatni račun

Besplatna dostava Overseas kurirskom službom iznad 69.99 €

DPD kurir 3.99 € Pošta 4.99 € Overseas 4.99 € Box Now 4.49 € GLS 4.99 € DPD točka 3.49 € GLS paketomat 3.99 €

Kontakt

Kako kupovati

Pomoć

Moj račun

▸ Prazno :-(

Besplatna dostava putem Box Now paketomata i Overseas kurirske službe iznad 69,99 €.

Loss Given Default Modeling: a Comparative Analysis

Name: Loss Given Default Modeling: a Comparative Analysis
Brand: LAP Lambert Academic Publishing
SKU: 18715689
Price: 25.92 EUR
Availability: InStock
Author: Olga Yashkir
ISBN: 9786202093750

Olga Yashkir

Yuri Yashkir

Jezik

Engleski

Knjiga Meki uvez

Libristo kod: 18715689

Nakladnici LAP Lambert Academic Publishing, studeni 2016

Several most popular Loss Given Default (LGD) models were investigated (LSM, Tobit, Three-Tiered Tob... Cijeli opis

Libristo kod: 18715689

63 b

25.92 €

Vanjske zalihe Šaljemo za 5-8 dana

30 dana za povrat kupljenih proizvoda

Kupci su kupili i

Charite sacerdotale, ou Lecons elementaires de theologie pastorale. Tome 2 DESURMONT-A

Meki uvez

35.24 €

Les droits sexuels ou la destruction programmée de l’enfance et de la famille Sigaut

Meki uvez

25.31 €

Die Sünde aber gebiert den Tod Andrea Schacht

Meki uvez

9.11 €

Carte des Vignobles et Vins de France - Affiche 50x70cm

binding.

29.56 €

leben // gestalten Ulf Kilian

Tvrdi uvez

21.56 €

Several most popular Loss Given Default (LGD) models were investigated (LSM, Tobit, Three-Tiered Tobit, Beta Regression, Inflated Beta Regression, Censored Gamma Regression) in order to compare their performance. We show that for a given input data set, the quality of the model calibration depends mainly on the proper choice of explanatory variables, but not on the fitting model. Model factors were chosen based on their correlation with historical LGDs of the calibration data set. Numerical values of non-quantitative parameters (industry, ranking, type of collateral) were introduced as their LGD average. We show that different debt instruments depend on different sets of model factors (from three factors for Revolving Credit or for Subordinated Bonds to eight factors for Senior Secured Bonds). Calibration of LGD models using distressed business cycle periods provide better fit than data from total available time span. Calibration algorithms and details of their realization using the R statistical package are presented. We demonstrate how LGD models can be used for stress testing. The results of this study can be of use to risk managers concerned with the Basel accord compliance.